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Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis.

Nataliia Molchanova^1,2,3,4, Alessandro Cagol^5,6,7,8, Mario Ocampo-Pineda^5,6,7

¹Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.

|September 19, 2025

Summary

This summary is machine-generated.

This study benchmarks automated detection of cortical lesions (CLs) in multiple sclerosis (MS) using MRI. The developed AI shows promise for improving CL analysis in clinical practice.

Keywords:

Brain Cortical lesions Deep learning Detection Magnetic Resonance Imaging Multiple sclerosis Segmentation Trustworthy AI

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Area of Science:

Neuroimaging
Artificial Intelligence
Biomarker Discovery

Background:

Cortical lesions (CLs) are key biomarkers in multiple sclerosis (MS) but are difficult to detect and segment in MRI.
Current methods lack standardization, hindering routine clinical use.

Purpose of the Study:

To establish a multi-centric benchmark for automated CL detection and segmentation in MRI.
To adapt and evaluate the nnU-Net framework for improved CL analysis.

Main Methods:

Utilized 656 multi-institutional MRI scans (3T and 7T) with MP2RAGE and MPRAGE sequences.
Employed the self-configuring nnU-Net framework with tailored adaptations for CL detection.
Performed out-of-distribution testing to assess model generalization.

Main Results:

Achieved an F1-score of 0.64 for in-domain and 0.5 for out-of-domain CL detection.
Analyzed model features and errors to understand AI decision-making.
Identified impacts of data variability and protocol differences on performance.

Conclusions:

The proposed approach demonstrates robust CL detection capabilities, addressing limitations in current MS diagnostics.
Findings offer recommendations for overcoming barriers to clinical adoption of automated MRI analysis.
Publicly accessible code and models will enhance reproducibility and future research.